本文旨在梳理作者学习路径，带领读者共同探索 GPU Kernel 性能分析从宏观到微观的技术演进。 引言 作为一名使用eBPF进行CPU性能分析的工程师，在转向学习GPU性能优化分析时，一直在思考GPU上是否有技术也可以实现用户自定义探针式性能分析？学习NVIDIA Nsight ...

通过更改数据加载器默认值（num_workers，batch_size，pin_memory，预取因子等）来充分利用GPU。 通过使用混合精度（fp16，bf16）最大 ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Back in 2000, Ian Buck and a small computer graphics team at Stanford University were watching the steady evolution of computer graphics processors for gaming and thinking about how such devices could ...

Support for unified memory across CPUs and GPUs in accelerated computing systems is the final piece of a programming puzzle that we have been assembling for about ten years now. Unified memory has a ...

The optimisation of GPU kernels through performance tuning and auto-tuning approaches has become essential in maximising computational efficiency on modern heterogeneous architectures. Researchers ...

Graphics processing units (GPUs) were originally designed to perform the highly parallel computations required for graphics rendering. But over the last couple of years, they’ve proven to be powerful ...

In an unprecedented move, Silicon Titan Nvidia has made the GPU kernel drivers for Linux open-source, paving the way for wider adoption of team green’s hardware beyond Windows. Nvidia is undoubtedly ...